{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 主代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../../\")\n",
    "import argparse\n",
    "import gc\n",
    "import os\n",
    "import random\n",
    "from typing import AnyStr\n",
    "from typing import List\n",
    "import ipdb\n",
    "from collections import defaultdict\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import torch\n",
    "import fitlog\n",
    "from torch.optim.lr_scheduler import LambdaLR\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import Subset\n",
    "from torch.utils.data import random_split\n",
    "from torch.optim import Adam\n",
    "from tqdm import tqdm\n",
    "from transformers import AdamW\n",
    "from transformers import DistilBertConfig\n",
    "from transformers import DistilBertTokenizer\n",
    "from transformers import DistilBertModel\n",
    "from transformers import BertConfig\n",
    "from transformers import BertTokenizer\n",
    "from transformers import BertModel\n",
    "from transformers import get_linear_schedule_with_warmup\n",
    "\n",
    "from datareader import *\n",
    "from metrics import *\n",
    "from model import *\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training on GPU\n",
      "====> True\n"
     ]
    }
   ],
   "source": [
    "    with open(\"./args.pkl\", 'rb') as fr:\n",
    "        args = pickle.load(fr)\n",
    "    args.model_dir = \"./test_tmp/\"\n",
    "    # Set all the seeds\n",
    "    seed = args.seed\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "\n",
    "\n",
    "    # See if CUDA available\n",
    "    device = torch.device(\"cpu\")\n",
    "    if args.n_gpu > 0 and torch.cuda.is_available():\n",
    "        print(\"Training on GPU\")\n",
    "        device = torch.device(\"cuda:0\")\n",
    "\n",
    "    # model configuration\n",
    "    batch_size = args.batch_size\n",
    "    lr = args.lr\n",
    "    weight_decay = args.weight_decay\n",
    "    n_epochs = args.n_epochs\n",
    "    args.full_bert = True\n",
    "    args.bertPath = \"../bert_en/\"\n",
    "\n",
    "    batch_size = args.batch_size\n",
    "    lr = args.lr\n",
    "    weight_decay = args.weight_decay\n",
    "    n_epochs = args.n_epochs\n",
    "    args.full_bert = True\n",
    "    print(\"====>\", args.full_bert)\n",
    "    bert_model = 'bert-base-uncased' if args.full_bert else 'distilbert-base-uncased'\n",
    "    if args.full_bert:\n",
    "        bert_config = BertConfig.from_pretrained(bert_model, num_labels=2) if args.bertPath is None else \\\n",
    "                        BertConfig.from_pretrained(args.bertPath, num_labels=2)\n",
    "        tokenizer = BertTokenizer.from_pretrained(bert_model) if args.bertPath is None else \\\n",
    "                        BertTokenizer.from_pretrained(args.bertPath)\n",
    "    else:\n",
    "        bert_config = DistilBertConfig.from_pretrained(bert_model, num_labels=2) if args.distillBertPath is None else \\\n",
    "                        DistilBertConfig.from_pretrained(args.distillBertPath, num_labels=2)\n",
    "        tokenizer = DistilBertTokenizer.from_pretrained(bert_model) if args.distillBertPath is None else \\\n",
    "                        DistilBertTokenizer.from_pretrained(args.distillBertPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "    train_set = NLIDataset(\"/home/hadoop/snli_1.0/snli_1.0_train.jsonl\", tokenizer=tokenizer)\n",
    "    val_set = NLIDataset(\"/home/hadoop/snli_1.0/snli_1.0_dev.jsonl\", tokenizer=tokenizer)\n",
    "    train_size = len(train_set)\n",
    "    val_size = len(val_set)\n",
    "    accs = []\n",
    "    Ps = []\n",
    "    Rs = []\n",
    "    F1s = []\n",
    "    # Store labels and logits for individual splits for micro F1\n",
    "    labels_all = []\n",
    "    logits_all = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at ../bert_en/ and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "    train_loader = DataLoader(\n",
    "        train_set,\n",
    "        batch_size=32,\n",
    "        shuffle=True,\n",
    "        collate_fn=collate_SNLI_batch_with_device(torch.device(\"cuda:0\"))\n",
    "    )\n",
    "    validation_evaluator = NLIEvaluator(val_set, torch.device(\"cuda:0\"))\n",
    "\n",
    "    log_dir = args.model_dir\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.system(\"mkdir %s\" % log_dir)\n",
    "    else:\n",
    "        os.system(\"rm -rf %s\" % log_dir)\n",
    "        os.system(\"mkdir %s\" % log_dir)\n",
    "    fitlog.set_log_dir(log_dir)\n",
    "    fitlog.add_hyper({\n",
    "            \"epochs\": n_epochs,\n",
    "            \"learning_rate\": lr,\n",
    "            \"warmup\": args.warmup_steps,\n",
    "            \"weight_decay\": weight_decay,\n",
    "            \"batch_size\": batch_size,\n",
    "            \"train_split_percentage\": args.train_pct,\n",
    "            \"bert_model\": bert_model,\n",
    "            \"seed\": seed,\n",
    "            \"pretrained_model\": args.pretrained_model,\n",
    "            \"tags\": \",\".join(args.tags)\n",
    "        }, name=args.run_name)\n",
    "\n",
    "    bert_config.num_labels = 3\n",
    "\n",
    "    # Create the model\n",
    "    if args.full_bert:\n",
    "        bert = BertForSequenceClassification.from_pretrained(\n",
    "                        bert_model, config=bert_config).to(device) if args.bertPath is None \\\n",
    "                else BertForSequenceClassification.from_pretrained(\n",
    "                        args.bertPath, config=bert_config).to(device)\n",
    "    else:\n",
    "        bert = DistilBertForSequenceClassification.from_pretrained(\n",
    "                    bert_model, config=bert_config).to(device) if args.distillBertPath is None \\\n",
    "                else DistilBertForSequenceClassification.from_pretrained(\n",
    "                        args.distillBertPath, config=bert_config).to(device)\n",
    "                \n",
    "    model = VanillaBert(bert).to(device)\n",
    "\n",
    "    # Create the optimizer\n",
    "    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "    optimizer_grouped_parameters = [\n",
    "        {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "         'weight_decay': weight_decay},\n",
    "        {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "    ]\n",
    "    optimizer = AdamW(optimizer_grouped_parameters, lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(\n",
    "        model: torch.nn.Module,\n",
    "        train_dls: List[DataLoader],\n",
    "        optimizer: torch.optim.Optimizer,\n",
    "        scheduler: LambdaLR,\n",
    "        validation_evaluator: NLIEvaluator,\n",
    "        n_epochs: int,\n",
    "        device: AnyStr,\n",
    "        log_interval: int = 1,\n",
    "        patience: int = 10,\n",
    "        model_dir: str = \"wandb_local\",\n",
    "        gradient_accumulation: int = 1,\n",
    "        domain_name: str = ''\n",
    "):\n",
    "    #best_loss = float('inf')\n",
    "    best_acc = 0.0\n",
    "    patience_counter = 0\n",
    "\n",
    "    epoch_counter = 0\n",
    "    total = sum(len(dl) for dl in train_dls)\n",
    "\n",
    "    # Main loop\n",
    "    while epoch_counter < n_epochs:\n",
    "        dl_iters = [iter(dl) for dl in train_dls]\n",
    "        dl_idx = list(range(len(dl_iters)))\n",
    "        finished = [0] * len(dl_iters)\n",
    "        i = 0\n",
    "        with tqdm(total=total, desc=\"Training\") as pbar:\n",
    "            while sum(finished) < len(dl_iters):\n",
    "                random.shuffle(dl_idx)\n",
    "                for d in dl_idx:\n",
    "                    domain_dl = dl_iters[d]\n",
    "                    batches = []\n",
    "                    try:\n",
    "                        for j in range(gradient_accumulation):\n",
    "                            batches.append(next(domain_dl))\n",
    "                    except StopIteration:\n",
    "                        finished[d] = 1\n",
    "                        if len(batches) == 0:\n",
    "                            continue\n",
    "                    optimizer.zero_grad()\n",
    "                    for batch in batches:\n",
    "                        model.train()\n",
    "                        batch = tuple(t.to(device) for t in batch)\n",
    "                        input_ids = batch[0]\n",
    "                        masks = batch[1]\n",
    "                        seg_ids = batch[2]\n",
    "                        labels = batch[3]\n",
    "                        # Testing with random domains to see if any effect\n",
    "                        #domains = torch.tensor(np.random.randint(0, 16, batch[3].shape)).to(device)\n",
    "                        domains = batch[4]\n",
    "                        rst = model(input_ids, token_ids=seg_ids, attention_mask=masks, domains=domains, labels=labels)\n",
    "\n",
    "                        loss, logits = rst.loss, rst.logits\n",
    "                        loss = loss / gradient_accumulation\n",
    "                        if i % log_interval == 0:\n",
    "                            fitlog.add_metric({\n",
    "                                \"Loss\": loss.item()\n",
    "                            }, step=i)\n",
    "                            print({\n",
    "                                \"Loss\": loss.item()\n",
    "                            })\n",
    "\n",
    "                        loss.backward()\n",
    "                        i += 1\n",
    "                        pbar.update(1)\n",
    "\n",
    "                    optimizer.step()\n",
    "                    if scheduler is not None:\n",
    "                        scheduler.step()\n",
    "\n",
    "        gc.collect()\n",
    "\n",
    "        # Inline evaluation\n",
    "        (val_loss, acc, P, R, F1), _ = validation_evaluator.evaluate(model)\n",
    "        print(f\"Validation acc: {acc}\")\n",
    "\n",
    "        #torch.save(model.state_dict(), f'{model_dir}/model_{domain_name}.pth')\n",
    "\n",
    "        # Saving the best model and early stopping\n",
    "        #if val_loss < best_loss:\n",
    "        if acc > best_acc:\n",
    "            best_model = model.state_dict()\n",
    "            #best_loss = val_loss\n",
    "            best_acc = acc\n",
    "            #wandb.run.summary['best_validation_loss'] = best_loss\n",
    "            torch.save(model.state_dict(), f'{model_dir}/model_{domain_name}.pth')\n",
    "            patience_counter = 0\n",
    "            # Log to wandb\n",
    "            fitlog.add_best_metric({\n",
    "                'Validation accuracy': acc,\n",
    "                'Validation Precision': P,\n",
    "                'Validation Recall': R,\n",
    "                'Validation F1': F1,\n",
    "                'Validation loss': val_loss})\n",
    "            print({\n",
    "                'Validation accuracy': acc,\n",
    "                'Validation Precision': P,\n",
    "                'Validation Recall': R,\n",
    "                'Validation F1': F1,\n",
    "                'Validation loss': val_loss})\n",
    "        else:\n",
    "            patience_counter += 1\n",
    "            # Stop training once we have lost patience\n",
    "            if patience_counter == patience:\n",
    "                break\n",
    "\n",
    "        gc.collect()\n",
    "        epoch_counter += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"emnlp_final_experiments/NLI/train_basic/model_None.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation:   0%|          | 0/308 [00:00<?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "Evaluation: 100%|██████████| 308/308 [00:18<00:00, 16.52it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((0.40941699057236897,\n",
       "  0.8470839260312945,\n",
       "  array([0.90227577, 0.87530562, 0.77526521]),\n",
       "  array([0.82245272, 0.86031841, 0.85842349]),\n",
       "  array([0.86051708, 0.86774731, 0.81472789])),\n",
       " [])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_evaluator.evaluate(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "    dev_set_1 = NLIDataset(\"/home/hadoop/multinli_1.0/multinli_1.0_dev_matched.jsonl\", tokenizer=tokenizer)\n",
    "    dev_set_2 = NLIDataset(\"/home/hadoop/multinli_1.0/multinli_1.0_dev_mismatched.jsonl\", tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "evaluator_dev1 = NLIEvaluator(dev_set_1, torch.device(\"cuda:0\"))\n",
    "evaluator_dev2 = NLIEvaluator(dev_set_2, torch.device(\"cuda:0\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 307/307 [00:28<00:00, 10.69it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((0.8974828775426076,\n",
       "  0.6163015792154865,\n",
       "  array([0.79388889, 0.72812726, 0.49647552]),\n",
       "  array([0.44475568, 0.57890198, 0.83445405]),\n",
       "  array([0.57011769, 0.644996  , 0.62255136])),\n",
       " [])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator_dev1.evaluate(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluation: 100%|██████████| 308/308 [00:29<00:00, 10.44it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((0.8697059124514654,\n",
       "  0.6268307567127747,\n",
       "  array([0.82242489, 0.7202381 , 0.50332551]),\n",
       "  array([0.47314815, 0.59399365, 0.82230745]),\n",
       "  array([0.60070533, 0.65105238, 0.62443878])),\n",
       " [])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evaluator_dev2.evaluate(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### 导入Sentiment 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "args.dataset_loc = \"./data/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "    all_dsets = [MultiDomainSentimentDataset(\n",
    "        args.dataset_loc,\n",
    "        [domain],\n",
    "        tokenizer\n",
    "    ) for domain in args.domains]\n",
    "    train_sizes = [int(len(dset) * args.train_pct) for j, dset in enumerate(all_dsets)]\n",
    "    val_sizes = [len(all_dsets[j]) - train_sizes[j] for j in range(len(train_sizes))]\n",
    "\n",
    "    accs = []\n",
    "    Ps = []\n",
    "    Rs = []\n",
    "    F1s = []\n",
    "    # Store labels and logits for individual splits for micro F1\n",
    "    labels_all = []\n",
    "    logits_all = []\n",
    "\n",
    "    for i in range(len(all_dsets)):\n",
    "        domain = args.domains[i]\n",
    "        test_dset = all_dsets[i]\n",
    "        # Override the domain IDs\n",
    "        k = 0\n",
    "        for j in range(len(all_dsets)):\n",
    "            if j != i:\n",
    "                all_dsets[j].set_domain_id(k)\n",
    "                k += 1\n",
    "        test_dset.set_domain_id(k)\n",
    "\n",
    "        # Split the data\n",
    "        if args.indices_dir is None:\n",
    "            subsets = [random_split(all_dsets[j], [train_sizes[j], val_sizes[j]])\n",
    "                       for j in range(len(all_dsets)) if j != i]\n",
    "            # Save the indices\n",
    "            with open(f'{args.model_dir}/train_idx_{domain}.txt', 'wt') as f, \\\n",
    "                    open(f'{args.model_dir}/val_idx_{domain}.txt', 'wt') as g:\n",
    "                for j, subset in enumerate(subsets):\n",
    "                    for idx in subset[0].indices:\n",
    "                        f.write(f'{j},{idx}\\n')\n",
    "                    for idx in subset[1].indices:\n",
    "                        g.write(f'{j},{idx}\\n')\n",
    "        else:\n",
    "            # load the indices\n",
    "            dset_choices = [all_dsets[j] for j in range(len(all_dsets)) if j != i]\n",
    "            subset_indices = defaultdict(lambda: [[], []])\n",
    "            with open(f'{args.indices_dir}/train_idx_{domain}.txt') as f, \\\n",
    "                    open(f'{args.indices_dir}/val_idx_{domain}.txt') as g:\n",
    "                for l in f:\n",
    "                    vals = l.strip().split(',')\n",
    "                    subset_indices[int(vals[0])][0].append(int(vals[1]))\n",
    "                for l in g:\n",
    "                    vals = l.strip().split(',')\n",
    "                    subset_indices[int(vals[0])][1].append(int(vals[1]))\n",
    "            subsets = [[Subset(dset_choices[d], subset_indices[d][0]), Subset(dset_choices[d], subset_indices[d][1])]\n",
    "                       for d in subset_indices]\n",
    "\n",
    "        train_dls = [DataLoader(\n",
    "            subset[0],\n",
    "            batch_size=batch_size,\n",
    "            shuffle=True,\n",
    "            collate_fn=collate_batch_transformer\n",
    "        ) for subset in subsets]\n",
    "\n",
    "        val_ds = [subset[1] for subset in subsets]\n",
    "        validation_evaluator = MultiDatasetClassificationEvaluator(val_ds, device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入NLI 数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### SNLI Test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Original Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from transformers import BertForNextSentencePrediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "    log_dir = args.model_dir\n",
    "    if not os.path.exists(log_dir):\n",
    "        os.system(\"mkdir %s\" % log_dir)\n",
    "    else:\n",
    "        os.system(\"rm -rf %s\" % log_dir)\n",
    "        os.system(\"mkdir %s\" % log_dir)\n",
    "    fitlog.set_log_dir(log_dir)\n",
    "    fitlog.add_hyper({\n",
    "            \"epochs\": n_epochs,\n",
    "            \"learning_rate\": lr,\n",
    "            \"warmup\": args.warmup_steps,\n",
    "            \"weight_decay\": weight_decay,\n",
    "            \"batch_size\": batch_size,\n",
    "            \"train_split_percentage\": args.train_pct,\n",
    "            \"bert_model\": bert_model,\n",
    "            \"seed\": seed,\n",
    "            \"pretrained_model\": args.pretrained_model,\n",
    "            \"tags\": \",\".join(args.tags)\n",
    "        }, name=args.run_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForNextSentencePrediction were not initialized from the model checkpoint at ../bert_en/ and are newly initialized: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "        # Create the model\n",
    "        if args.full_bert:\n",
    "            bert = BertForSequenceClassification.from_pretrained(\n",
    "                            bert_model, config=bert_config).to(device) if args.bertPath is None \\\n",
    "                    else BertForSequenceClassification.from_pretrained(\n",
    "                            args.bertPath, config=bert_config).to(device)\n",
    "        else:\n",
    "            bert = DistilBertForSequenceClassification.from_pretrained(\n",
    "                        bert_model, config=bert_config).to(device) if args.distillBertPath is None \\\n",
    "                    else DistilBertForSequenceClassification.from_pretrained(\n",
    "                            args.distillBertPath, config=bert_config).to(device)\n",
    "                    \n",
    "        model = VanillaBert(bert).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "        # Create the optimizer\n",
    "        no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']\n",
    "        optimizer_grouped_parameters = [\n",
    "            {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n",
    "             'weight_decay': weight_decay},\n",
    "            {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n",
    "        ]\n",
    "        # optimizer = Adam(optimizer_grouped_parameters, lr=1e-3)\n",
    "        # scheduler = None\n",
    "        optimizer = AdamW(optimizer_grouped_parameters, lr=lr)\n",
    "        scheduler = get_linear_schedule_with_warmup(\n",
    "            optimizer,\n",
    "            args.warmup_steps,\n",
    "            n_epochs * sum([len(train_dl) for train_dl in train_dls])\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def train(\n",
    "        model: torch.nn.Module,\n",
    "        train_dls: List[DataLoader],\n",
    "        optimizer: torch.optim.Optimizer,\n",
    "        scheduler: LambdaLR,\n",
    "        validation_evaluator: MultiDatasetClassificationEvaluator,\n",
    "        n_epochs: int,\n",
    "        device: AnyStr,\n",
    "        log_interval: int = 1,\n",
    "        patience: int = 10,\n",
    "        model_dir: str = \"wandb_local\",\n",
    "        gradient_accumulation: int = 1,\n",
    "        domain_name: str = ''\n",
    "):\n",
    "    #best_loss = float('inf')\n",
    "    best_acc = 0.0\n",
    "    patience_counter = 0\n",
    "\n",
    "    epoch_counter = 0\n",
    "    total = sum(len(dl) for dl in train_dls)\n",
    "\n",
    "    # Main loop\n",
    "    while epoch_counter < n_epochs:\n",
    "        dl_iters = [iter(dl) for dl in train_dls]\n",
    "        dl_idx = list(range(len(dl_iters)))\n",
    "        finished = [0] * len(dl_iters)\n",
    "        i = 0\n",
    "        with tqdm(total=total, desc=\"Training\") as pbar:\n",
    "            while sum(finished) < len(dl_iters):\n",
    "                random.shuffle(dl_idx)\n",
    "                for d in dl_idx:\n",
    "                    domain_dl = dl_iters[d]\n",
    "                    batches = []\n",
    "                    try:\n",
    "                        for j in range(gradient_accumulation):\n",
    "                            batches.append(next(domain_dl))\n",
    "                    except StopIteration:\n",
    "                        finished[d] = 1\n",
    "                        if len(batches) == 0:\n",
    "                            continue\n",
    "                    optimizer.zero_grad()\n",
    "                    for batch in batches:\n",
    "                        model.train()\n",
    "                        batch = tuple(t.to(device) for t in batch)\n",
    "                        input_ids = batch[0]\n",
    "                        masks = batch[1]\n",
    "                        seg_ids = batch[2]\n",
    "                        labels = batch[3]\n",
    "                        # Testing with random domains to see if any effect\n",
    "                        #domains = torch.tensor(np.random.randint(0, 16, batch[3].shape)).to(device)\n",
    "                        domains = batch[4]\n",
    "\n",
    "                        rst = model(input_ids, token_ids=seg_ids, attention_mask=masks, domains=domains, labels=labels)\n",
    "                        loss, logits = rst.loss, rst.logits\n",
    "                        loss = loss / gradient_accumulation\n",
    "                        if i % log_interval == 0:\n",
    "                            fitlog.add_metric({\n",
    "                                \"Loss\": loss.item()\n",
    "                            }, step=i)\n",
    "                            print({\n",
    "                                \"Loss\": loss.item()\n",
    "                            })\n",
    "\n",
    "                        loss.backward()\n",
    "                        i += 1\n",
    "                        pbar.update(1)\n",
    "\n",
    "                    optimizer.step()\n",
    "                    if scheduler is not None:\n",
    "                        scheduler.step()\n",
    "\n",
    "        gc.collect()\n",
    "\n",
    "        # Inline evaluation\n",
    "        (val_loss, acc, P, R, F1), _ = validation_evaluator.evaluate(model)\n",
    "        print(f\"Validation acc: {acc}\")\n",
    "\n",
    "        #torch.save(model.state_dict(), f'{model_dir}/model_{domain_name}.pth')\n",
    "\n",
    "        # Saving the best model and early stopping\n",
    "        #if val_loss < best_loss:\n",
    "        if acc > best_acc:\n",
    "            best_model = model.state_dict()\n",
    "            #best_loss = val_loss\n",
    "            best_acc = acc\n",
    "            #wandb.run.summary['best_validation_loss'] = best_loss\n",
    "            torch.save(model.state_dict(), f'{model_dir}/model_{domain_name}.pth')\n",
    "            patience_counter = 0\n",
    "            # Log to wandb\n",
    "            fitlog.add_best_metric({\n",
    "                'Validation accuracy': acc,\n",
    "                'Validation Precision': P,\n",
    "                'Validation Recall': R,\n",
    "                'Validation F1': F1,\n",
    "                'Validation loss': val_loss})\n",
    "            print({\n",
    "                'Validation accuracy': acc,\n",
    "                'Validation Precision': P,\n",
    "                'Validation Recall': R,\n",
    "                'Validation F1': F1,\n",
    "                'Validation loss': val_loss})\n",
    "        else:\n",
    "            patience_counter += 1\n",
    "            # Stop training once we have lost patience\n",
    "            if patience_counter == patience:\n",
    "                break\n",
    "\n",
    "        gc.collect()\n",
    "        epoch_counter += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training:   0%|          | 0/675 [00:00<?, ?it/s]Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "Training:   0%|          | 1/675 [00:00<03:37,  3.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Loss': 0.6424081325531006}\n"
     ]
    },
    {
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      "\r",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Loss': 0.8092383742332458}\n",
      "{'Loss': 0.6692293882369995}\n"
     ]
    },
    {
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     ]
    },
    {
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      "{'Loss': 0.8715680837631226}\n"
     ]
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      "{'Loss': 0.7069055438041687}\n"
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      "Training:  96%|█████████▌| 647/675 [02:44<00:07,  3.83it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Loss': 0.0009648799896240234}\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
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     ]
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     "text": [
      "{'Loss': 0.0028055012226104736}\n"
     ]
    },
    {
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      "\r",
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     ]
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     "text": [
      "{'Loss': 0.0010542571544647217}\n"
     ]
    },
    {
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      "\r",
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      "{'Loss': 0.000944674015045166}\n"
     ]
    },
    {
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      "\r",
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     ]
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      "{'Loss': 0.001850426197052002}\n",
      "{'Loss': 0.0012178421020507812}\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
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      "{'Loss': 0.0012477636337280273}\n"
     ]
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    {
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      "\r",
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     ]
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     "text": [
      "{'Loss': 0.0011192262172698975}\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
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      "{'Loss': 0.0006712675094604492}\n"
     ]
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    {
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      "\r",
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      "{'Loss': 0.0016124248504638672}\n"
     ]
    },
    {
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      "\r",
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     "text": [
      "{'Loss': 0.000984281301498413}\n"
     ]
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    {
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      "\r",
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      "{'Loss': 0.0011755526065826416}\n"
     ]
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    {
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      "\r",
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      "{'Loss': 0.0010591745376586914}\n"
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      "{'Loss': 0.0017719864845275879}\n"
     ]
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    {
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      "{'Loss': 0.0011244714260101318}\n"
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      "{'Loss': 0.0017295777797698975}\n"
     ]
    },
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      "\r",
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      "{'Loss': 0.0027607381343841553}\n"
     ]
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    {
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     "text": [
      "{'Loss': 0.001827239990234375}\n",
      "{'Loss': 0.001849830150604248}\n"
     ]
    },
    {
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     "output_type": "stream",
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      "Training:  99%|█████████▉| 667/675 [02:49<00:01,  6.09it/s]"
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    {
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     "output_type": "stream",
     "text": [
      "{'Loss': 0.0019265711307525635}\n",
      "{'Loss': 0.0036190450191497803}\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
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      "{'Loss': 0.0010601282119750977}\n"
     ]
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    {
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      "\r",
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      "{'Loss': 0.0015368163585662842}\n"
     ]
    },
    {
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      "\r",
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    },
    {
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     "output_type": "stream",
     "text": [
      "{'Loss': 0.0019803643226623535}\n"
     ]
    },
    {
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     "output_type": "stream",
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      "\r",
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    {
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     "output_type": "stream",
     "text": [
      "{'Loss': 0.00394892692565918}\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\r",
      "Training: 100%|█████████▉| 672/675 [02:50<00:00,  3.83it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Loss': 0.0011147558689117432}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Training: 100%|█████████▉| 673/675 [02:51<00:00,  3.49it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Loss': 0.0006954073905944824}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "Training: 100%|█████████▉| 674/675 [02:51<00:00,  3.29it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Loss': 0.0009052455425262451}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training: 100%|██████████| 675/675 [02:51<00:00,  3.93it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Loss': 0.001306980848312378}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Evaluation: 100%|██████████| 25/25 [00:02<00:00,  9.11it/s]\n",
      "Evaluation: 100%|██████████| 25/25 [00:02<00:00,  9.16it/s]\n",
      "Evaluation: 100%|██████████| 25/25 [00:01<00:00, 12.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation acc: 0.9216666666666666\n",
      "{'Validation accuracy': 0.9216666666666666, 'Validation Precision': 0.9228295819935691, 'Validation Recall': 0.9258064516129032, 'Validation F1': 0.9243156199677939, 'Validation loss': 0.3589379945397377}\n"
     ]
    }
   ],
   "source": [
    "        train(\n",
    "            model,\n",
    "            train_dls,\n",
    "            optimizer,\n",
    "            scheduler,\n",
    "            validation_evaluator,\n",
    "            n_epochs,\n",
    "            device,\n",
    "            args.log_interval,\n",
    "            model_dir=args.model_dir,\n",
    "            gradient_accumulation=args.gradient_accumulation,\n",
    "            domain_name=domain\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### MetaSelf Train Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from tqdm import tqdm\n",
    "import torch\n",
    "from copy import deepcopy\n",
    "from typing import List\n",
    "from torch import nn\n",
    "from transformers import BertForSequenceClassification\n",
    "import numpy as np\n",
    "from torch.nn import CrossEntropyLoss, MSELoss\n",
    "from torch.nn import functional as F\n",
    "import random\n",
    "from torch.autograd import Variable\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from datareader import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from MetaSelfTrain import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "class InstanceReweighting(BertForSequenceClassification):\n",
    "    def __init__(self, config, weak_set, few_shot_set,\n",
    "                 weak_set_label, weak_set_weights=None,\n",
    "                 lr4model=2e-2, scale_lr4model=1e-3, coeff4expandset=1.0,\n",
    "                 batch_size=5):\n",
    "        super(InstanceReweighting, self).__init__(config)\n",
    "        self.weak_set = weak_set\n",
    "        self.weak_set_size = len(self.weak_set) if weak_set is not None else 0\n",
    "        self.weak_set_weights = weak_set_weights\n",
    "        self.weak_set_label = weak_set_label\n",
    "        self.few_shot_set = few_shot_set\n",
    "        self.expand_data_list = None\n",
    "        self.lr4model = lr4model\n",
    "        self.coefff4expandset = coeff4expandset\n",
    "        self.scale_lr4model = scale_lr4model\n",
    "        self.bert_optim = torch.optim.SGD([\n",
    "            {'params': self.bert.parameters(), 'lr': self.lr4model * scale_lr4model}\n",
    "        ])\n",
    "        self.batch_size = batch_size\n",
    "        self.model_device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "        self.to(self.model_device)\n",
    "\n",
    "    def ConstructExpandData(self, expand_set, expand_idxs, batch_size=100, convey_fn=None):\n",
    "        if convey_fn is not None:\n",
    "            expand_set = convey_fn(expand_set)\n",
    "        self.expand_data_list = [expand_set.collate_raw_batch(\n",
    "                [expand_set[idx] for idx in expand_idxs[i:min(i+batch_size, len(expand_set))] ]\n",
    "        ) for i in range(0, len(expand_idxs), batch_size)]\n",
    "\n",
    "    def SampleBatch(self, indices=None, batch_size=-1, dataset=None):\n",
    "        batch_size = self.batch_size if batch_size == -1 else batch_size\n",
    "        if dataset is None:\n",
    "            if indices is None:\n",
    "                indices = random.sample(list(range(len(self.weak_set))), batch_size)\n",
    "            items = [self.weak_set[idx] for idx in indices]\n",
    "            batch = self.weak_set.collate_raw_batch(items)\n",
    "        else:\n",
    "            if indices is None:\n",
    "                indices = random.sample(list(range(len(dataset))), batch_size)\n",
    "            items = [dataset[idx] for idx in indices]\n",
    "            batch = dataset.collate_raw_batch(items)\n",
    "        return batch, torch.tensor(indices, dtype=torch.int64).to(self.model_device)\n",
    "\n",
    "    def Prediction(self, batch, model=None):\n",
    "        input_ids = batch[0]\n",
    "        masks = batch[1]\n",
    "        labels = batch[2]\n",
    "        if model is not None:\n",
    "            outputs = model.bert(\n",
    "                input_ids=input_ids, attention_mask=masks, token_type_ids=None,\n",
    "                position_ids=None, head_mask=None, inputs_embeds=None,\n",
    "                output_attentions=None, output_hidden_states=None, return_dict=None\n",
    "            )\n",
    "        else:\n",
    "            outputs = self.bert(\n",
    "                input_ids=input_ids, attention_mask=masks, token_type_ids=None,\n",
    "                position_ids=None, head_mask=None, inputs_embeds=None,\n",
    "                output_attentions=None, output_hidden_states=None, return_dict=None\n",
    "            )\n",
    "        pooled_output = outputs[1]\n",
    "        pooled_output = self.dropout(pooled_output) if model is None else model.dropout(pooled_output)\n",
    "        logits = self.classifier(pooled_output) if model is None else model.classifier(pooled_output)\n",
    "        return logits\n",
    "\n",
    "    def ModelLoss(self, batch, model):\n",
    "        logits = self.Prediction(batch, model)\n",
    "        labels = batch[2]\n",
    "        loss = None\n",
    "        if labels is not None:\n",
    "            if self.num_labels == 1:\n",
    "                #  We are doing regression\n",
    "                loss_fct = MSELoss()\n",
    "                loss = loss_fct(logits.view(-1), labels.view(-1), reduction=\"none\")\n",
    "            else:\n",
    "                loss = F.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1), reduce=False)\n",
    "        return loss\n",
    "\n",
    "    def LossAndAcc(self, batch, model):\n",
    "        logits = self.Prediction(batch, model)\n",
    "        labels = batch[2]\n",
    "        loss = None\n",
    "        if labels is not None:\n",
    "            if self.num_labels == 1:\n",
    "                #  We are doing regression\n",
    "                loss_fct = MSELoss()\n",
    "                loss = loss_fct(logits.view(-1), labels.view(-1), reduction=\"none\")\n",
    "            else:\n",
    "                loss = F.cross_entropy(logits.view(-1, self.num_labels), labels.view(-1), reduce=False)\n",
    "        acc = accuracy_score(labels, logits.argmax(dim=1))\n",
    "        return loss, acc\n",
    "\n",
    "    def ModelGrads(self, batch, weights, model, create_graph=True):\n",
    "        loss = self.ModelLoss(batch, model)\n",
    "        cost = torch.sum(loss * weights)\n",
    "        grads = torch.autograd.grad(cost, params(model),\n",
    "                                    create_graph=create_graph,\n",
    "                                    retain_graph=False)\n",
    "        return grads\n",
    "\n",
    "    def ComputeGrads4Weights(self, step, batch, weights, tmp_model, few_shot_data):\n",
    "        model_grads = self.ModelGrads(batch, weights, tmp_model)\n",
    "        update_params(tmp_model, self.lr4model, source_params=model_grads)\n",
    "        loss, acc = self.LossAndAcc(few_shot_data, tmp_model)\n",
    "        print('####Few Shot %3d | %3d ####, loss/acc = %6.8f/%6.7f' % (\n",
    "            step, self.weak_set_size, loss, acc\n",
    "        ))\n",
    "        if self.expand_data_list is not None:\n",
    "            grad_weights = torch.autograd.grad(loss, weights, only_inputs=True, retain_graph=True)[0]\n",
    "            for batch_idx, expand_data in enumerate(self.expand_data_list):\n",
    "                e_loss, e_acc = self.LossAndAcc(expand_data, tmp_model)\n",
    "                print('####Expand Few Shot  %3d | %3d ####, loss/acc = %6.8f/%6.7f' % (\n",
    "                    step, len(self.weak_set), e_loss, e_acc\n",
    "                ))\n",
    "                loss = self.coefff4expandset * e_loss\n",
    "                if (batch_idx+1) == len(self.expand_data_list):\n",
    "                    tmp_grad_weights = torch.autograd.grad(loss, weights, only_inputs=True,\n",
    "                                                    create_graph=False, retain_graph=False)[0]\n",
    "                else:\n",
    "                    tmp_grad_weights = torch.autograd.grad(loss, weights, only_inputs=True,\n",
    "                                                           retain_graph=True)[0]\n",
    "                grad_weights += tmp_grad_weights\n",
    "        else:\n",
    "            grad_weights = torch.autograd.grad(loss, weights, only_inputs=True,\n",
    "                                                create_graph=False, retain_graph=False)[0]\n",
    "        return grad_weights\n",
    "\n",
    "    def Valid(self, all_metrics=False, expand_data_by_logits=False):\n",
    "        self.eval()\n",
    "        labels = []\n",
    "        preds = []\n",
    "        data_loader = DataLoader(self.weak_set,\n",
    "                                 batch_size=self.batch_size,\n",
    "                                 shuffle=False,\n",
    "                                 collate_fn=self.weak_set.collate_raw_batch\n",
    "                                 )\n",
    "        with torch.no_grad():\n",
    "            for batch in data_loader:\n",
    "                pred = self.Prediction(batch)\n",
    "                torch.cuda.empty_cache()\n",
    "                preds.append(pred)\n",
    "                labels.append(batch[-2])\n",
    "            pred_tensor = torch.cat(preds, dim=0)\n",
    "            pred_tensor = pred_tensor[:len(self.weak_set)]\n",
    "            epsilon = torch.ones([len(pred_tensor), 2], dtype=torch.float32, device=self.model_device) * 1e-8\n",
    "            pred_tensor = (pred_tensor - epsilon).abs()  # to avoid the prediction [1.0, 0.0],\n",
    "            # which leads to the 'nan' value in log operation\n",
    "            label_tensor = torch.tensor(self.weak_set_label)\n",
    "            val_acc = accuracy_score(label_tensor.numpy(),\n",
    "                                     pred_tensor.cpu().argmax(dim=1).numpy())\n",
    "            val_loss = F.nll_loss(pred_tensor.to(self.model_device).log(),\n",
    "                              label_tensor.to(self.model_device))\n",
    "        if expand_data_by_logits:\n",
    "            if self.expand_set is None:\n",
    "                self.expand_set = self.few_shot_set.__class__()\n",
    "            probs, model_predictions = pred_tensor.max(dim=1)\n",
    "            weak_data_y = pred_tensor.__gt__(0.5).int().tolist()\n",
    "            indexs = probs.__gt__(0.99)\n",
    "            if indexs.int().sum() > 20:\n",
    "                _, indexs = probs.topk(20)\n",
    "            new_idxs = indexs.cpu().tolist()\n",
    "            for idx in new_idxs:\n",
    "                ID = self.weak_set.data_ID[idx]\n",
    "                if ID in self.expand_set.data_ID:\n",
    "                    e_idx = self.expand_set.data_ID.index(ID)\n",
    "                    self.expand_set.data_y[e_idx] = weak_data_y[idx]\n",
    "                else:\n",
    "                    self.expand_set.data_ID.append(ID)\n",
    "                    self.expand_set.data_len.append(self.weak_set.data_len[idx])\n",
    "                    self.expand_set.data_y.append(weak_data_y[idx])\n",
    "                    self.expand_set.data.update({ID:self.weak_set.data[ID]})\n",
    "            print(\"expand set:\", len(self.expand_set))\n",
    "        self.train()\n",
    "        if all_metrics:\n",
    "            val_prec = precision_score(label_tensor.numpy(),\n",
    "                                       pred_tensor.cpu().argmax(dim=1).numpy())\n",
    "            val_recall = recall_score(label_tensor.numpy(),\n",
    "                                      pred_tensor.cpu().argmax(dim=1).numpy())\n",
    "            val_f1 = f1_score(label_tensor.numpy(),\n",
    "                              pred_tensor.cpu().argmax(dim=1).numpy())\n",
    "            return val_acc, val_loss, val_prec, val_recall, val_f1\n",
    "        else:\n",
    "            return val_acc, val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "few_shot_set, unlabeld_set = DataSplit(test_dset, [100, len(test_dset)-100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "weak_set_label = unlabeld_set.dataset['label'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "model = InstanceReweighting(bert_config, unlabeld_set, few_shot_set,\n",
    "                 weak_set_label, weak_set_weights=None,\n",
    "                 lr4model=2e-2, scale_lr4model=1e-3, coeff4expandset=1.0,\n",
    "                 batch_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def WeakLabeling(model, data, batch_size=20):\n",
    "    with torch.no_grad():\n",
    "        preds_list = []\n",
    "        for batch in tqdm(DataLoader(data, \n",
    "                                batch_size=batch_size, \n",
    "                                shuffle=False, \n",
    "                                collate_fn=collate_batch_transformer_with_device(model.model_device))):\n",
    "            logits = model.Prediction(batch)\n",
    "            preds_list.append(logits)\n",
    "        pred_tensor = torch.cat(preds_list).softmax(dim=1)\n",
    "    torch.cuda.empty_cache()\n",
    "    vals, idxs = pred_tensor.sort(dim=1)\n",
    "    weak_label = (pred_tensor > 0.5).long().tolist()\n",
    "    data.data_y = weak_label\n",
    "    # print(\"===> weak label logits:\", pred_tensor.max(dim=1)[0].tolist())\n",
    "    entrophy = (pred_tensor.log().abs() * pred_tensor).sum(dim=1)\n",
    "    return entrophy, idxs[:, 1], vals[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:11<00:00,  1.18s/it]\n"
     ]
    }
   ],
   "source": [
    "rst = WeakLabeling(model, dataset, batch_size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_recall_fscore_support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.902,\n",
       " (array([0.89566929, 0.90853659]),\n",
       "  array([0.91 , 0.894]),\n",
       "  array([0.90277778, 0.90120968]),\n",
       "  array([500, 500])))"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IMDB\n",
    "preds = rst[1].cpu().numpy()\n",
    "labels = dataset.dataset['label'].values\n",
    "accuracy_score(labels, preds), precision_recall_fscore_support(labels, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8328690807799443,\n",
       " (array([0.80628272, 0.86309524]),\n",
       "  array([0.8700565, 0.7967033]),\n",
       "  array([0.83695652, 0.82857143]),\n",
       "  array([177, 182])))"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentiment140\n",
    "preds = rst[1].cpu().numpy()\n",
    "labels = dataset.dataset['label'].values\n",
    "accuracy_score(labels, preds), precision_recall_fscore_support(labels, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 30/30 [00:18<00:00,  1.59it/s]\n"
     ]
    }
   ],
   "source": [
    "rst = WeakLabeling(model, unlabeld_set, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "preds = rst[1].cpu().numpy()\n",
    "labels = weak_set_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9173684210526316,\n",
       " (array([0.92004264, 0.91476091]),\n",
       "  array([0.91322751, 0.92146597]),\n",
       "  array([0.91662241, 0.9181012 ]),\n",
       "  array([945, 955])))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(labels, preds), precision_recall_fscore_support(labels, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<datareader.MultiDomainSentimentDataset at 0x7f2b8c064ef0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_dsets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['books', 'dvd', 'electronics', 'kitchen_&_housewares']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args.domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "from collections import OrderedDict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "state_dict = torch.load(\"./emnlp_sentiment_experiments/basic_distilbert/model_kitchen_&_housewares.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "new_state_dict = OrderedDict([(k[5:], v) for k, v in state_dict.items()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(new_state_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": []
  }
 ],
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